"... Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. T ..."

Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.

...nerated according to the following decision rule: ⎧ ⎨ 1 if |D(x)| > τ B(x) = ⎩ 0 otherwise, We denote this algorithm as “simple differencing”. Often the threshold τ is chosen empirically. =-=Rosin [66], [67] -=-surveyed and reported experiments on many different criteria for choosing τ. Smits and Annoni [68] discussed how the threshold can be chosen to achieve application-specific requirements for false ala...

"... This paper presents a methodology for evaluating the performance of video surveillance tracking systems. We introduce a novel framework for performance evaluation using pseudo-synthetic video, which employs data captured online and stored in a surveillance database. Tracks are automatically sel ..."

This paper presents a methodology for evaluating the performance of video surveillance tracking systems. We introduce a novel framework for performance evaluation using pseudo-synthetic video, which employs data captured online and stored in a surveillance database. Tracks are automatically selected from the surveillance database and then used to generate ground truthed video sequences with a controlled level of perceptual complexity that can be used to quantitatively characterise the quality of the tracking algorithms.

"... Identifying moving objects is a critical task for many computer vision applications; it provides a classification of the pixels into either foreground or background. A common approach used to achieve such classification is background removal. Even though there exist numerous of background removal a ..."

Identifying moving objects is a critical task for many computer vision applications; it provides a classification of the pixels into either foreground or background. A common approach used to achieve such classification is background removal. Even though there exist numerous of background removal algorithms in the literature, most of them follow a simple flow diagram, passing through four major steps, which are pre-processing, background modelling, foreground detection and data validation. In this paper, we survey many existing schemes in the literature of background removal, surveying the common pre-processing algorithms used in different situations, presenting different background models, and the most commonly used ways to update such models and how they can be initialized. We also survey how to measure the performance of any moving object detection algorithm, whether the ground truth data is available or not, presenting performance metrics commonly used in both cases.

... in the frame and all the frames in the sequence. The value is usually determined experimentally based on a large database. In the latter case, the threshold is adapted according to some rules. Rosin =-=[135, 136]-=- surveyed and reported experiments on many different criteria for choosing the threshold. Smits and Annoni [137] discussed how the threshold can be chosen to achieve application-specific requirements ...

"... In this paper, a novel parametric and global image histogram thresholding method is presented. It is based on the estimation of the statistical parameters of “object ” and “background ” classes by the expectation–maximization (EM) algorithm, under the assumption that these two classes follow a gener ..."

In this paper, a novel parametric and global image histogram thresholding method is presented. It is based on the estimation of the statistical parameters of “object ” and “background ” classes by the expectation–maximization (EM) algorithm, under the assumption that these two classes follow a generalized Gaussian (GG) distribution. The adoption of such a statistical model as an alternative to the more common Gaussian model is motivated by its attractive capability to approximate a broad variety of statistical behaviors with a small number of parameters. Since the quality of the solution provided by the iterative EM algorithm is strongly affected by initial conditions (which, if inappropriately set, may lead to unreliable estimation), a robust initialization strategy based on genetic algorithms (GAs) is proposed. Experimental results obtained on simulated and real images confirm the effectiveness of the proposed method.

"... Abstract—In this work we consider an event-driven wireless visual sensor network (WVSN) comprised of untethered camera nodes and scalar sensors deployed in a hostile environment. In the event-driven paradigm, each camera node transmits a surveillance frame to the cluster-head only if an event of int ..."

Abstract—In this work we consider an event-driven wireless visual sensor network (WVSN) comprised of untethered camera nodes and scalar sensors deployed in a hostile environment. In the event-driven paradigm, each camera node transmits a surveillance frame to the cluster-head only if an event of interest was captured in the frame, for energy and bandwidth conservation. We thus examine a simple image processing algorithm at the camera nodes based on difference frames and the chi-squared detector. We show that the test statistic of the chi-squared detector is equivalent to that of a robust (non-parametric) detector and that this simple algorithm performs well on indoor surveillance sequences and some, but not all, outdoor sequences. In outdoor sequences containing significant changes in background and lighting, this simple detector may produce a high probability of error and benefits from the inclusion of scalar sensor decisions. The scalar sensor decisions are, however, prone to attack and may exhibit errors that are arbitrarily frequent, pervasive throughout the network and difficult to predict. To achieve attack prediction and mitigation given an attacker whose actions are not known a priori, we employ game-theoretic analysis. We show that the scalar sensor error can be controlled through cluster-head checking and appropriate selection of cluster size. Given this attack mitigation, we employ real-life sequences to determine the total probability of error when individual and combined decisions are utilized and we discuss the ensuing ramifications and performance issues. Index Terms—Actuation, event-detection, game theory, scalarsensors, sensor network security, wireless visual sensor networks

...d non-event frames. We note that the definition of event and non-event is largely application-dependent, as is the domain in which the processing is carried out (spatial, temporal or frequency-based) =-=[24]-=-. In contrast, a WVSN camera node collects incoming images containing unknown objects which may or may not enter into the frame at any time [6], [25]. Since we do not know ahead of time what objects w...

"... We present HiLight, a new form of real-time screen-camera com-munication without showing any coded images (e.g., barcodes) for off-the-shelf smart devices. HiLight encodes data into pixel translu-cency change atop any screen content, so that camera-equipped de-vices can fetch the data by turning the ..."

We present HiLight, a new form of real-time screen-camera com-munication without showing any coded images (e.g., barcodes) for off-the-shelf smart devices. HiLight encodes data into pixel translu-cency change atop any screen content, so that camera-equipped de-vices can fetch the data by turning their cameras to the screen. Hi-Light leverages the alpha channel, a well-known concept in com-puter graphics, to encode bits into the pixel translucency change. By removing the need to directly modify pixel RGB values, Hi-Light overcomes the key bottleneck of existing designs and enables real-time unobtrusive communication while supporting any screen content. We build a HiLight prototype using off-the-shelf smart devices and demonstrate its efficacy and robustness in practical set-tings. By offering an unobtrusive, flexible, and lightweight com-munication channel between screens and cameras, HiLight opens up opportunities for new HCI and context-aware applications, e.g., smart glasses communicating with screens to realize augmented re-ality.

by
Thierry Pécot, Charles Kervrann
- The International Workshop on Local and Non-Local Approximation in Image Processing, 2008

"... Change detection between two images is challenging and needed in a wide variety of imaging applications. Several approaches have been yet developed, especially methods based on difference image. In this paper, we propose an original patch-based Markov modeling framework to detect spatial irregularit ..."

Change detection between two images is challenging and needed in a wide variety of imaging applications. Several approaches have been yet developed, especially methods based on difference image. In this paper, we propose an original patch-based Markov modeling framework to detect spatial irregularities in the difference image with low false alarm rates. Experimental results show that the proposed approach performs well for change detection, especially for images with low signal-to-noise ratios. 1.

...atic thresholding methods can be classified into gray-level distribution based [4] and spatial properties based [5]. A review of image difference followed by thresholding based methods is proposed in =-=[5, 6, 7]-=-. More sophisticated approaches have been also developed; i) predictive models [8] exploit the relationships between nearby pixels both in space and time (when an image sequence is available); ii) met...

by
Pierre-marc Jodoin, Max Mignotte, Janusz Konrad
- In Proceedings of the IEEE International Conference on Image Processing

"... In this paper, we propose a light and fast pixel-based statistical motion detection method based on a background subtraction pro-cedure. The statistical representation of the background relies on its spatial color distributions herein modeled by a mixture of Gaus-sians. The Gaussian parameters are o ..."

In this paper, we propose a light and fast pixel-based statistical motion detection method based on a background subtraction pro-cedure. The statistical representation of the background relies on its spatial color distributions herein modeled by a mixture of Gaus-sians. The Gaussian parameters are obtained after segmenting one reference frame with an unsupervised Bayesian approach whose parameter estimation step is ensured by the -Means and the It-erated Conditional Estimation (ICE) algorithms. Since the motion detection function only depends on a global mixture of Gaus-sians, only a few bits per pixel need to be stored in memory. Our method achieves real-time performances, especially when look up tables are used to store pre-calculated data. Results have been ob-tained on synthetic and real video sequences and compared with other statistical methods. Index Terms — Image motion analysis, Object detection 1.

...e the ones comparing intensity changes between frames. The intensity difference is usually computed between two successive frames or between a frame and a reference image containing no moving objects =-=[1, 2, 3]-=-. The intensity difference is then thresholded with predetermined global threshold. Although adaptive thresholds [4, 5] can be used, these methods are sensitive to phenomena that violate the basic ass...

...te or black pixels, also according to the human criterion. 8. The four binary images are compared against the corresponding ground-truth by computing the Correct Classification Percentage (PCC) index =-=[37]-=-: TW + TB PCC = TW + TB + FW + FB (3) where TW (true whites) and TB (true blacks) are the number of white/black pixels respectively in the image that are also white/black in the ground-truth; FW (fals...

"... The CANDELA project aims at realizing a system for real-time image processing in traffic and surveillance applications. The system performs segmentation, labels the extracted blobs and tracks their movements in the scene. Performance evaluation of such a system is a major challenge since no standard ..."

The CANDELA project aims at realizing a system for real-time image processing in traffic and surveillance applications. The system performs segmentation, labels the extracted blobs and tracks their movements in the scene. Performance evaluation of such a system is a major challenge since no standard methods exist and the criteria for evaluation are highly subjective. This paper proposes a performance evaluation approach for video content analysis (VCA) systems and identifies the involved research areas. For these areas we give an overview of the state-of-the-art in performance evaluation and introduce a classification into different semantic levels. The proposed evaluation approach compares the results of the VCA algorithm with a ground-truth (GT) counterpart, which contains the desired results. Both the VCA results and the ground truth comprise description files that are formatted in MPEG-7. The evaluation is required to provide an objective performance measure and a mean to choose between competitive methods. In addition, it enables algorithm developers to measure the progress of their work at the different levels in the design process. From these requirements and the state-of-the-art overview we conclude that standardization is highly desirable for which many research topics still need to be addressed.